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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
library(ipctools)
```
# ipctools: Utilities to Support Integrated Food Security Phase Classification (IPC) Data Processing and Visualisation
<!-- badges: start -->
[](https://www.repostatus.org/#wip)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://github.com/nutriverse/ipctools/actions/workflows/R-CMD-check.yaml)
[](https://github.com/nutriverse/ipctools/actions/workflows/test-coverage.yaml)
[](https://app.codecov.io/gh/nutriverse/ipctools?branch=main)
[](https://www.codefactor.io/repository/github/nutriverse/ipctools)
<!-- badges: end -->
The [Integrated Food Security Phase Classification (IPC)](https://www.ipcinfo.org/) is a widely used tool for classifying and analysing the severity and magnitude of food insecurity and malnutrition situations in various countries and regions around the world. It provides a common understanding of the food security situation and enables decision-makers to take appropriate actions to mitigate and respond to food crises. This package provides functions and utilities that support IPC-related data analysis and visualisation.
## What does `ipctools` do?
Please note that `ipctools` is still highly experimental and is undergoing a lot of development. Hence, any functionalities described below have a high likelihood of changing interface or approach as we aim for a stable working version.
Currently, the package provides utility functions that support the processing, validation, and analysis of acute malnutrition datasets based on IPC data standards and recommendations. Over time, it is aimed that other similar data processing, validation, and analysis steps recommended by IPC on datasets for acute food insecurity and chronic food insecurity can be incorporated.
In addition, it is planned to include a set of functions that would wrap around the [IPC-CH API](https://www.ipcinfo.org/ipc-country-analysis/api/) enabling programmatic access to available resources from the IPC for researchers and analysts who use R. For this purpose, a formal request for access to the API has been submitted. Approval of this request will determine whether this plan will come to fruition.
## Installation
`ipctools` is not yet on CRAN but can be installed from the [nutriverse R universe](https://nutriverse.r-universe.dev) as follows:
```{r install, eval = FALSE}
install.packages(
"ipctools",
repos = c("https://nutriverse.r-universe.dev", "https://cloud.r-project.org")
)
```
## Usage
### Performing checks on MUAC dataset
For nutrition survey datasets that include MUAC measurements, the IPC recommends that the following tests be performed:
1. **Age ratio test** - The ratio between those whose age is less than 30 months to those who are 30 months and above.
2. **Sex ratio test** - The male to female sex ratio test checks whether the ratio of the number of males to the number of females in a survey sample is similar to an expected ratio. It is usually assumed that there should be equal numbers of males and females in the survey sample.
3. **Digit preference score** - Digit preference is the observation that the final number in a measurement occurs with a greater frequency than is expected by chance. This can occur because of rounding, the practice of increasing or decreasing the value in a measurement to the nearest whole or half unit, or because data are made up.
4. **Standard deviation** - The standard deviation of the MUAC measurements.
`ipctools` provides a set of functions that operate on a given anthropometric dataset containing at least age, sex, and MUAC measurements of children (if present, oedema information is also processed) to provide an assessment of data quality.
The following diagram illustrates how this process/workflow using the `ipctools` functions:
```{r ipctools-workflow, echo = FALSE, fig.align = "center"}
knitr::include_graphics("https://github.com/nutriverse/ipctools/assets/5742010/68f0425a-ec51-48f0-8cd2-c0ea50f18e6f")
```
The functions in `ipctools` are pipe-friendly hence the workflow described above can be done through piped operations as follows:
```{r ipc-checks}
## Check for missing data ----
muac_data |>
process_muac_data() |>
check_missing_data()
## Check data quality ----
muac_data |>
process_muac_data() |>
ipc_muac_check(
muac_units = "cm",
oedema_recode = c(1, 2)
)
```
### Calculating acute malnutrition prevalence on a MUAC dataset
The IPC-recommended approach to calculating prevalence of acute malnutrition based on MUAC is to perform a weighted analysis when either the age ratio test or the sex ratio test is problematic. For example, based on the MUAC check shown above, the example dataset `muac_data` has some issues with its age ratio and sex ratio. To calculate acute malnutrition prevalence from this dataset, a weighted analysis will have to be implemented. This can be done using the `ipc_calculate_prevalence()` function as follows:
```{r ipc-prevalence}
muac_data |>
process_muac_data() |>
ipc_muac_check(
muac_units = "cm",
oedema_recode = c(1, 2),
.summary = FALSE
) |>
ipc_calculate_prevalence()
```
## Citation
If you find the `ipctools` package useful please cite using the suggested citation provided by a call to the `citation()` function as follows:
```{r citation}
citation("ipctools")
```
## Community guidelines
Feedback, bug reports and feature requests are welcome; file issues or seek support [here](https://github.com/nutriverse/ipctools/issues). If you would like to contribute to the package, please see our [contributing guidelines](https://nutriverse.io/ipctools/CONTRIBUTING.html).
This project is released with a [Contributor Code of Conduct](https://nutriverse.io/ipctools/CODE_OF_CONDUCT.html). By participating in this project you agree to abide by its terms.